Systems and methods for predicting hvac filter change

ABSTRACT

Computer-implemented systems and methods for estimating a replacement status of an HVAC air filter. Outdoor weather data (e.g., outdoor temperature information), is obtained. A Total Runtime Value of the HVAC system is determined based upon the obtained outdoor weather data. Finally, a replacement status of the air filter is estimated as a function of a comparison of the Total Runtime Value with a Baseline Value. By correlating air filter replacement status with an estimated runtime of the HVAC system, a credible predictor of air filter usage is provided. By estimating fan runtime based on easily-obtained outdoor weather data, the methods are readily implemented with any existing HVAC system and do not require installation of sensors or other mechanical or electrical components to the HVAC system.

BACKGROUND

The present disclosure relates to air filters for HVAC systems. Moreparticularly, it relates to systems and methods for predicting the needto change or replace an air filter in an HVAC system, such as in aresidential, demand-operation HVAC system.

Heating, ventilation, and air conditioning (HVAC) systems are commonlyused to control temperature and air quality in the interior space ofvarious dwellings, such as homes, buildings and other structures. Withmany HVAC installations, a disposable air filter is conventionallyemployed. Such filters often include a frame and a fibrous filtermaterial, and may include a reinforcing structure to help support thefilter material. After a period of use, these filters become dirty orclogged and must be replaced. Proper filter maintenance helps keep theHVAC system operating at maximum efficiency, reduces operating costs,and better ensures desired indoor air quality; further, continuing torun an HVAC system with an excessively clogged filter can negativelyaffect the expected useful life of various HVAC system components.

Many non-expert HVAC system users (e.g., homeowners) cannot readilyascertain whether the air filter needs to be replaced by simpleinspection and/or do not regularly inspect the air filter. To help suchusers avoid the problems described above, filter manufacturers recommendreplacement on a regular, fixed-interval basis, and in particular afixed calendar period of time. The fixed interval replacement approachis easy to remember and follow, and the recommended time period istypically based on extensive studies of air filter performance undernormal conditions. With this approach, the filter is replaced after acertain recommended fixed calendar period of time, such as three months,has passed. This fixed period of time, however, may not be appropriatefor all situations, and in particular with demand-operation HVAC systems(typically employed with residential homes and light commercialdwellings) in which the HVAC system's fan only runs (and thus airflow ispresented to the air filter) when the controller is calling for heatingor cooling. Under these circumstances, the actual runtime of the HVACsystem over the course of the fixed calendar period of time will oftenvary with the season of the year. As a result, the fixed period may betoo short, in which case the air filter is discarded prematurely, or thefixed period may be too long, in which case the air filter is usedbeyond the time when it should have been changed. Numerous otherenvironmental factors (e.g., airborne particulate levels, userpreferences, etc.) may further contribute to deviations between actualfilter loading vs. expected at the end of the recommended fixed periodof time.

Regardless of whether the recommended fixed period is too short or toolong as compared to the actual runtime of the HVAC system, some HVACsystem users (e.g., a homeowner) may view the recommended three month(or other) fixed interval filter change as being overly cautious anddecide not to follow the recommendation. Others may simply forget tomake record of the recommended replacement date. Absent an overtindication that the HVAC system is not operating as expected due to apossibly clogged filter, many users will purposefully or unintentionallynot change the air filter until after the air filter is beyond itsuseful life.

In light of the above, various devices have been devised that indicate areplacement status or need for filter change based on an actualcondition of the filter. For example, some filter manufacturers providecolor-change indicia or dirty filter pictures/illustrations on the airfilter frame and/or packaging to indicate the stage at which the airfilter needs to be changed. While these filter change strategies attemptto more closely tie the filter change to actual filter condition, theyare not very able to accommodate different kinds of indoor aircontaminants that may greatly affect the visual appearance of the airfilter. Further, these techniques require the user to remember tovisually inspect the air filter in order to determine status.

In other HVAC installations, a digital thermostat is provided,programmed to generate filter change reminders based on actual runhours. The thermostat directly controls the HVAC system and can readilytrack the number of hours the air filter has processed indoor air; thethermostat's display can indicate to the user when the air filter needsto be changed. Several models of room air cleaners also employ a similarchange strategy, using run hours to indicate the need for change. Thisfilter change approach can be a viable option for those users alreadyowning a programmed or programmable digital thermostat. However, thepurchase and retro-fit installation to a conventional HVAC system (thatdoes not otherwise include an appropriate digital thermostat) can beexpensive and time-consuming.

Another known approach for generating filter status information ispremised upon a sensed or monitored pressure drop across the air filter.The pressure drop will increase as the air filter becomes clogged withparticles, such that an elevated pressure drop indicates a need toreplace the air filter. One such system is provided as after-marketproduct (for installation to an existing HVAC system), and includes amechanical filter pressure indicator that must be installed between theblower and the filter where a slight vacuum exists and indicates thefilter pressure drop to the user. While viable, the user is required toform one or more holes into the HVAC ductwork (e.g., one or more drilledholes) and to preform initial calibration. Due to the numerous variantsof HVAC equipment and installation methods, different filters, differentairflow in heating and cooling modes, the need for sensor installation,and frequent lack of readily accessible electrical power near the HVACsystem, these and other aftermarket indicators are less than optimal.

In light of the above, a need exists for a simple, low-cost system andmethod of more accurately predicting and communicating the need forfilter change in an HVAC system, such as demand-type HVAC systemapplications.

SUMMARY

Some aspects of the present disclosure relate to computer-implementedmethods for estimating or predicting a replacement status of an airfilter in an HVAC system, such as an HVAC system operating in adwelling. The method includes obtaining outdoor weather data for anoutdoor environment related to the HVAC system. A Total Runtime Value ofa fan of the HVAC system is determined based upon the obtained outdoorweather data. Finally, a replacement status of the air filter isestimated as a function of a comparison of the Total Runtime Value witha Baseline Value. By correlating air filter replacement status with anestimated runtime of the HVAC system fan, more accurate and crediblepredictors of air filter usage are provided. Moreover, by estimating fanruntime based on easily-obtained outdoor weather data, the methods ofthe present disclosure are readily implemented with any existing HVACsystem and do not require installation of sensors or other mechanical orelectrical components to the HVAC system. In some embodiments, theoutdoor weather data is electronically obtained from a knownweather-related date source (e.g., an online data service such as awebsite). In yet other embodiments, outdoor temperature and otherweather-related parameters are considered as part of the Total RuntimeValue determination. In yet other embodiments, the outdoor weather dataincludes outdoor temperature information and is obtained at regularintervals; a Current Runtime is estimated for each interval based uponthe obtained outdoor temperature information. The Baseline Value can beprovided in various manners, and in some embodiments is based upon, oradjusted in accordance with, parameters relevant to the particular airfilter and/or HVAC system operating conditions, such as dwellingparameters, HVAC use parameters, user preference parameters, and filterparameters.

Other aspects of the present disclosure are directed towardcomputer-implemented systems for predicting a replacement status of anair filter installed in an HVAC system, capable of implementing themethods described above. In some embodiments, the systems of the presentdisclosure include a computing device, such as a mobile smart phone, acomputer, a computer network, etc., programmed or prompted to performthe methods of the present disclosure.

Yet other aspects of the present disclosure relate tocomputer-implemented methods for estimating HVAC system air filterreplacement status. Outdoor weather data for a geographical regionrelated to the HVAC system is obtained (e.g., electronically retrievedfrom an online data service). The replacement status of the air filteris approximated using the outdoor weather data. For example, the outdoorweather data is used to estimate air filter runtime, and the air filterruntime is used to estimate the replacement status of the air filter.Related aspects of the present disclosure are directed towardcomputer-implemented systems with one or more processors configured toobtain the outdoor weather data and to use the obtained weather data toapproximate the replacement status of the air filter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of an illustrative HVAC system located withina dwelling;

FIG. 2 is a schematic view of a system for estimating a replacementstatus of an air filter in accordance with principles of the presentdisclosure;

FIG. 3 is a graph illustrating examples of residential HVAC runtime as afunction of daily average temperature;

FIG. 4 is a flow chart of a method for estimating a replacement statusof an air filter in accordance with principles of the presentdisclosure;

FIG. 5 is a flow chart of another method of estimating a replacementstatus of an air filter in accordance with principles of the presentdisclosure;

FIG. 6 is a flow chart of another method of estimating a replacementstatus of an air filter in accordance with principles of the presentdisclosure;

FIG. 7 is a flow chart of another method of estimating a replacementstatus of an air filter in accordance with principles of the presentdisclosure; and

FIGS. 8A and 8B are graphs illustrating a comparison of HVAC air filterperformance using calendar-based filter change techniques with optimizedfilter change techniques in accordance with principles of the presentdisclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to systems and methods forestimating or predicting HVAC air filter replacement status andoptionally indicating a need for change (or other information) to auser. The systems and methods of the present disclosure can be employedwith virtually any type of HVAC installation, but are particularlybeneficial with existing, forced air HVAC systems operating on a demandbasis (i.e., the systems blower or fan only operates when the system isin cooling or heating mode) such as those commonly found in residentialor light commercial dwellings. As a point of reference, FIG. 1schematically illustrates a dwelling 20 having an installed HVAC system22 (referenced generally). Conventionally, a structure of the dwelling20 establishes an interior 24, commonly referred to as “indoor” or“indoor environment”, and generally separates or isolates indoor airfrom an external environment 26 of the dwelling 20 (also referred to as“outdoor” or “outdoor environment”). The term “dwelling” is in referenceto any enclosed structure in which one or more persons live, temporarilyreside, seek shelter, work, store belongings, etc., such as a house(e.g., single family home, bungalow, duplex, row house, farm house,villa, cabin, etc.), an attached multi-unit housing (e.g., apartment,condominium, townhouse, etc.), a store, an office space, a building, awarehouse, etc. In some embodiments, the dwellings of the presentdisclosure are in reference to residential homes and light commercialinstallations as those terms are commonly understood. The HVAC system 22operates to treat indoor airflow, and includes ductwork 30, a fan orblower 32 and an air filter 34. One or more HVAC components 36 are alsoprovided, and can assume various forms (e.g., furnace, air conditioner,humidifier, etc.). The ductwork 30 (e.g., supply ducts and return ducts)is open to the interior 24 of the dwelling 20, arranged to directairflow from the interior 24 to the HVAC component(s) 36, and then backto the interior 24 with operation of the fan 32. One or more thermostats38 or similar controllers dictate operation of the HVAC system 22, suchas by activating the fan 32 and the HVAC component(s) 36 in response tovarious conditions, such as sensed indoor temperature. Regardless,airflow within the HVAC system 22 passes through the air filter 34.

The air filter 34 can assume a variety of forms, and is generallyconfigured to remove dust, debris and other particles (e.g., optionallyfine particles having a diameter of 2.5 μm or less (“PM_(2.5)”)) fromthe indoor air of the dwelling 20. Over time, as the level of particlescaptured by the air filter 34 continues to increase, the air filter 34may detrimentally restrict air flow through the HVAC system 22 andshould be replaced. The present disclosure provides systems and methodsfor predicting the replacement status (or need for replacement) of theair filter 34, and can beneficially be employed with an existing HVACsystem. In some embodiments, the systems and methods of the presentdisclosure operate to provide a meaningful filter replacement predictionwithout reference to any sensors, or other mechanical or electricalcomponents, connected to the HVAC system 22 or elsewhere inside thedwelling 20.

The systems and methods of the present disclosure can be implemented byone or more computing devices, one non-limiting example of which isshown in block form in FIG. 2 at 100. The computing device 100 canassume various forms known in the art and capable of electronicallyexecuting the operations described below. For example, the computingdevice 100 can be, or can be provided as part of, a mobile device (e.g.,mobile smart phone, tablet computer, personal digital assistant (PDA),laptop computer, media player, etc.) or a non-mobile device (desktopcomputer, computer network server, cloud server, etc.). In more generalterms, the computing device 100 includes one or more processors 102configured to operate according to executable instructions (i.e.,program code), a memory 104, and a communications module 106. Theprocessor 102 is electronically connected to the memory 104, and maystore information within and subsequently retrieve stored informationfrom the memory 104. The memory 104 can be of a conventional format,such as one or more of random-access memory (RAM), static random-accessmemory (SRAM), read only memory (ROM), erasable programmable read-onlymemory (EPROM), flash drive, hard drive, etc. The communications module106 can include a transmitter and a receiver as are known in the art,and is configured to provide wireless communications with other devicesor systems, such as one or more of a cloud server 120, a network 122(e.g., a public computer network such as the internet), a computerserver 124, a mobile device 126, etc., via a communication interfacesuch as, but not limited to, high-frequency radio frequency (RF)signals. As described below, aspects of the present disclosure includeelectronically retrieving weather data from a weather-related datasource or service. The communications module 106 can interface with theweather data source via any of the devices or systems 120-126 or anyother appropriate system. FIG. 2 reflects the weather-related datasource at 128. The computing device 100 may include additional discretelogic or analog circuitry not shown in FIG. 2. Further, the computingdevice 100 can optionally include other components and/or modules, suchas a user interface module 129 (e.g., a display screen), one or moreuser input devices (not shown) and associated programming as are knownin the art (e.g., touchscreen, keyboard, buttons, mouse, cursor), etc.

With the above basic construction of the computing device 100 in mind,aspects of the present disclosure relate to operational steps providedor embodied by a prediction module 130 included with the computingdevice 100 and that are described in greater detail below. Theprediction module 130 is configured (e.g., executable program codewritten in any known programming language such as Java, C++, and thelike) to provide instructions or algorithms to be executed by theprocessor 102 as described below. In some embodiments, an operatingsystem 132 executes on the processor 102 and provides an operatingenvironment for the prediction module 130 (e.g., the prediction module130 can be provided as an “app”). The prediction module 130 can compriseexecutable program code stored in a computer-readable storage device(e.g., the memory 104) for execution by the processor 102. As otherexamples, the prediction module 130 can comprise software, firmware or,in some examples, may be implemented in discrete logic. In otherexamples, the techniques described below may be executed by specificallyprogrammed circuitry of the processor 102.

With additional reference to FIG. 1, the prediction module 130 operates(or causes the computing device 100 to operate) to predict a replacementstatus of the air filter 34 otherwise installed in the HVAC system 22 asa function of runtime of the fan 32, with this fan runtime, in turn,being estimated based, at least in part, on obtained informationindicative of temperature of the outdoor environment 26 (“outdoortemperature”). The term “replacement status” relates to a remaininguseful life of the air filter 34. As particles are captured over time,the air filter 34 will increasingly restrict airflow through the HVACsystem 22; while the HVAC system 22 can readily operate at an acceptableefficiency level with some elevated airflow restriction at the airfilter 34, at some point in time, the restriction to airflow presentedby the air filter 34 overly taxes the HVAC system 22. As particles arecaptured over time, some filters may experience a decrease in filterefficiency. Under either of these circumstances, the air filter 34 isunderstood as no longer being useful or as having reached the end of itsuseful life. At the end of the useful life, then, the air filter 34should be replaced. The “replacement status” as estimated or predictedby the prediction module 130 implicates whether the air filter 34 hasreached the end of its useful life (and thus should be replaced) and/orhow much of the useful life of the air filter 34 remains (e.g.,expressed as a percentage of the useful life).

By estimating replacement status as a function of fan runtime, thesystems and methods of the present disclosure leverage a correlationbetween the number of hours the air filter 34 has processed indoor air(and thus fan runtime) and an actual condition of the air filter 34. Inother words, the predicted replacement status can be tied to fan runtimewith a high degree of confidence. However, the systems and methods ofthe present disclosure do not require a sensor or other electronicand/or mechanical device connected to the HVAC system 22 for monitoringfan runtime (or any other parameter such as pressure drop across the airfilter 34). Instead, the systems and methods of the present disclosureuniquely utilize information relating to or implicating outdoortemperature (and optionally other weather-related and/ornon-weather-related information) to estimate fan runtime, with theoutdoor temperature information being obtained from a remote source. Asa point of reference, it has been surprisingly discovered that a strongcorrelation exists between fan runtime and the temperature differenceinside and outside the dwelling 22 (ΔT). A study was performed onapproximately 100 homes in each of the northern and southern climates ofthe United States to determine the impact of weather variables onresidential HVAC runtime when the systems were run in demand (automatic)mode. For both heating and cooling seasons, it was surprisinglydiscovered that by far the dominant weather effect on fan runtime wasΔT. Also showing some statistical significance in one or both seasonswere solar insolation, rainfall, and a wind/temperature interaction(i.e., wind alone was not found to be significant, but cold wind duringheating or hot wind during cooling was). FIG. 3 shows the combinedsummer and winter data where each data point represents a state'saverage data for one day (four states were included in the study, twoeach in both the north and the south; due to a very mild winterresulting in a small sample size, the winter data for homes in Floridais excluded). FIG. 3 illustrates a very strong correlation betweenheating and cooling runtime and the daily outdoor temperature.

With the above operational parameters in mind, FIG. 4 is a flow diagramillustrating techniques provided by the prediction module 130 (FIG. 2)and consistent with the present disclosure. In general terms, thetechniques entail retrieving or obtaining outdoor weather data (e.g.,outdoor temperature information) at 150. The obtained outdoor weatherdata is used to approximate the replacement status of the air filter 34(FIG. 1). For example, a Total Runtime Value is determined at 152 basedupon the obtained outdoor weather data. The Total Runtime Value relatesto or is indicative of a total length of time the fan 32 (FIG. 1) hasoperated. A replacement status of the air filter 34 (FIG. 1) isestimated at 154 based on the Total Runtime Value (and thus based on theobtained outdoor weather data). In some embodiments, the replacementstatus is estimated or predicted as a function of the determined TotalRuntime Value, such as by comparing the Total Runtime Value with aBaseline Value as described below. The obtained weather data can bestored in raw data form, or optionally can be used to estimate a runtimeof the fan over a current time period (to which the obtained weatherdata applies), with sequential ones of the so-estimated current fanruntimes being correlated with one another (e.g., summed) to determinethe Total Runtime Value. In some embodiments, the current fan runtimeand/or the Baseline Value can be estimated as a function of, or adjustedin accordance with, information implicating one or more other parametersrelevant to the dwelling 20 and/or the HVAC system 22 (in addition tooutdoor temperature). The techniques of the present disclosure can beimplemented on a loop-type basis, with the Total Runtime Value beingrecalculated or “updated” each time outdoor weather data is retrieved.The present disclosure is in no way limited to any one particular loopmethodology. As such, the filter status prediction techniques of FIG. 4generally reflect a looped or repeating analysis; non-limitingtechniques for accomplishing a loop analysis are provided elsewhere.

FIG. 5 is a flow diagram of exemplary methods of the present disclosure.With additional reference to FIGS. 1 and 2, a filter predictionoperation is initiated at 200. The prediction module 130 can beconfigured or programmed to perform prediction functions by establishingand tracking certain values including “Total Runtime”. The Total RuntimeValue is indicative or representative of the total length of time thefan 32 has been “on” or running with the particular air filter 34 inplace. The Total Runtime Value can be expressed as a length of time(e.g., estimated actual runtime of the fan 32 in terms of minutes,hours, etc.). In other embodiments, the Total Runtime Value canrepresent a variable other than length of time, but that directlycorrelates with fan runtime. For example and as described in greaterdetail below, the Total Runtime Value can be based upon unitless heatingdegree day and/or cooling degree day values that in turn implicate howoften the fan 32 is likely to have run. Regardless, at initiation 200 ofthe filter prediction operation, the Total Runtime Value is set to 0.

Outdoor weather data is obtained at 202. The outdoor weather dataprovides weather-related information relevant to the outdoor environment26 of the dwelling 20 (e.g., at a locale or geographical region of thedwelling 20 and thus of the HVAC system 22), and includes at leastoutdoor temperature information indicative of temperature at the outdoorenvironment 26 of the dwelling 20 over a designated period of time (or“Time Interval”), for example 24 hours (or one day). Otherweather-related information is optionally also obtained as describedbelow. The obtained outdoor temperature information can be an actualoutdoor temperature(s) relevant to the outdoor environment 26 over theTime Interval, can be historical outdoor temperature(s) relevant to theoutdoor environment 26 over the Time Interval at the same calendar dayin a previous year, can be forecasted outdoor temperature(s) relevant tothe outdoor environment 26 over the Time Interval, can be heating degreeday (HDD) value(s), can be cooling degree day (CDD) value(s), etc.Regardless, the outdoor weather data, and in particular the outdoortemperature information, is obtained by electronically communicatingwith a data source or provider (e.g., a third party data source) thatotherwise records and/or forecasts information indicative of outdoortemperatures (e.g., temperature in degrees, HDD value, CDD value, etc.).For example, the processor 102 can be prompted to operate thecommunications module 106 to obtain or fetch data from a weather-relatedonline data service (e.g., website) via the internet. The communicationinterface includes, but is not limited to, any wired or wirelessshort-range and long-range communication interfaces. The short-rangecommunication interfaces may be, for example, local area network (LAN),interfaces conforming to a known communication standard, such asBluetooth standard, IEEE 802 standards (e.g., IEEE 802.11), a ZigBee orsimilar specification, such as those based on the IEEE 802.15.4standard, or other public or proprietary protocol. The long-rangecommunication interfaces may be, for example, wide area network (WAN),cellular network interfaces, satellite communication interfaces, etc.The communication interface may be either within a private computernetwork, such as an intranet, or on a public computer network, such asthe internet. Other communication interfaces or protocols can includecode division multiple access (CDMA), Global System for MobileCommunications (GSM), Enhanced Data GMS Environment (EDGE), High-SpeedDownlink Packet Access (HSDPA), a protocol for email, instant messaging(IM) and/or a short message service (SMS).

The obtained outdoor temperature information can be a temperatureexpressed in conventional terms, such as degrees Fahrenheit or degreesCelsius. In some embodiments, the obtained outdoor temperatureinformation is a single temperature value relevant to the particularTime Interval (e.g., the obtained outdoor temperature information can bean average temperature over the course of the Time Interval, a hightemperature of the Time Interval, a low temperature of the TimeInterval, etc.). Alternatively, the obtained outdoor temperatureinformation can be a plurality of temperature values relevant to theparticular Time Interval. Under these circumstances, the predictionmodule 130 can be configured or programmed to determine an averagetemperature from the plurality of temperature values, with the averagetemperature being utilized with the subsequent analyses described below.Alternatively, the prediction module 130 can be configured or programmedto act upon each of the plurality of temperature values individually(e.g., where the obtained outdoor information associates a time stampwith each temperature value) commensurate with the explanations below.In yet other embodiments, the obtained outdoor temperature informationcan be or include an HDD or CDD value, or some other parameter, value,etc. other than a temperature (but indicative of or related totemperature).

Where the source of weather-related information is a data service (e.g.,website) accessed via the internet, the prediction module 130 can beconfigured or programmed to “recognize” or fetch desired informationfrom the website using conventional programming logic. For example, theprediction module 130 can be programmed with the format and layout of aknown data service (e.g., a weather website) in mind, capable ofinterrogating and extracting, parsing or fetching the desired data orinformation from one or more webpages of the designated website (e.g.,programmed to extract, parse or fetch data from the Hypertext MarkupLanguage (HTML), Wireless Markup Language (WML), or other languageutilized in composing the designated webpage(s) or other onlinecontent). The source of weather-related information is not limited towebsites, and can be any other format or service. For example, outdoorweather data can be electronically obtained from a personal weatherstation.

In some embodiments, the source (e.g., a third party source) ofweather-related information is specifically directed to only a locale ofthe dwelling 20 (e.g., the dwelling 20 is located in a particular townor city, and the source of weather-related information is a websitededicated to that same town or city). In other embodiments, the sourceof weather-related information provides weather data for multipledifferent locales. Under these circumstances, the prediction module 130can be configured or programmed to retrieve weather data relevant onlyto a locale of the dwelling, such as by searching for data applicable toa zip code of the dwelling 20, a name of the city or town in which thedwelling 20 is located, etc. In these and similar embodiments, thesystems and methods of the present disclosure optionally provide for theobtaining or retrieval of locale information (e.g., city or town name,zip code, longitude and latitude coordinates, etc.) relevant to thedwelling 20, including receiving locale information from a user (e.g.,in response to prompts or inquiries initiated by the prediction module130), reference to GPS information generated at or delivered to thecomputing device 100, etc. In some embodiments, the zip code can beobtained by GPS on, for example, a mobile device.

Optionally at 204, a Current Runtime of the fan 32 relevant to the TimeInterval is estimated and is or reflects an estimate of the hours orminutes (or other time increment) the fan 32 operated (or was “on”)during the Time Interval. The Current Runtime can be estimated basedupon at least the obtained outdoor temperature information. In someembodiments, the Current Runtime is estimated based solely upon theobtained outdoor temperature information. In other embodiments, theCurrent Runtime is estimated based upon the obtained outdoor temperatureinformation along with other factors, such as other informationassociated with the obtained outdoor weather data, dwelling information,user information, etc., as described below. With respect to the outdoortemperature information component of the Current Runtime estimation, theprediction module 130 is configured or programmed, in some embodiments,to include or derive an algorithm correlating the outdoor temperatureinformation with an expected runtime of the fan 32. For example, thealgorithm(s) acted upon by prediction module 130 can be akin to theequations implicated by the data of in FIG. 3. A runtime percentage canbe derived from the outdoor temperature information; the Current Runtimeis then determined as the product of the runtime percentage and the TimeInterval (e.g., if the runtime percentage is determined to be 50% for aTime Interval of 24 hours (or 1440 minutes), the Current Runtime is 12hours (or 720 minutes)). A number of other algorithms or othertechniques for deriving an estimated Current Runtime for the fan 32based upon at least the obtained outdoor temperature information areenvisioned by the present disclosure. For example, the prediction module130 can be programmed to access or act upon a look-up table thatcorrelates pre-determined Current Runtime values to possible outdoortemperatures. Alternatively, where the outdoor temperature informationis an HDD or CDD value, the Current Runtime can be estimated via analgorithm incorporating the HDD or CDD value, or the HDD/CDD value cansimply be designated or assigned as the Current Runtime (e.g., theCurrent Runtime can be the unitless HDD or CDD value). In other words,systems and methods of the present disclosure do not require the step ofestimating a Current Runtime from the obtained weather data; instead theobtained outdoor weather data can be stored as a raw number and used todetermine the Total Runtime Value as described below. In someembodiments, the algorithm(s) may involve “predicting future HVACruntime” so as to recommend a date by which the filter is likely toreach is end of life. This can be done, for example, with combination ofhistorical data and user runtime history.

In other embodiments, the prediction module 130 can be configured toincorporate one or more additional variables (in addition to informationindicative of outdoor temperature) with the algorithm(s) and/or inselecting a best fit algorithm from a plurality of available algorithms.For example, one or more indoor temperature set points selected by aparticular user can affect actual runtime of the fan 32 (e.g., where theoutdoor temperature over the Time Interval is 20° F. (heating mode), thefan 32 will run more frequently when the HVAC system indoor temperatureset point is 75° F. as compared to an indoor temperature set point of65° F.). The prediction module 130 can optionally be configured orprogrammed to account for this user-preference indoor temperature setpoint factor, for example by being programmed with one or morealgorithms that include the indoor temperature set point(s) as avariable, by selecting a particular algorithm that corresponds with theparticular indoor set point(s), etc. A single indoor temperature setpoint can be acted upon, or multiple heating or cooling indoortemperature set points (e.g., daytime and nighttime set points, weekendand weekday set points, etc.) can be accounted for. With these and otherembodiments, the prediction module 130 can further be configured orprogrammed to receive indoor temperature set point information from theuser.

Additionally or alternatively, the Current Runtime can be estimated as afunction of, or adjusted in accordance with, one or moredwelling-related parameters, examples of which are provided below. Insome embodiments, the prediction module 130 is configured to receiveinformation from the user (or other sources such as electronic onlinedata services (e.g., website) as described above) implicating theparticular dwelling-related parameter. The so-received information canbe employed as a variable in one or more Current Runtime estimationalgorithms and/or can be used to adjust a determined, preliminaryCurrent Runtime (e.g., the determined preliminary Current Runtime can bepredicated upon an assumed level or value of the particulardwelling-related parameter; where the actual level or value of thedwelling-related parameter varies from the assumed level or value, thedetermined preliminary Current Runtime can be adjusted accordingly toarrive at the Current Runtime used in subsequent operational steps).Exemplary dwelling-related parameters include, but are not limited to:shading (e.g., where the dwelling 20 is in an heavily shadedenvironment, an actual runtime of the fan 32 in cooling modes will beless than expected, and in heating modes will be greater than expected);square footage of the dwelling 20 and heating/cooling capacity of theHVAC system 22 (e.g., depending upon the HVAC system capacity, the fan32 may operate for extended periods of time when installed at a largerdwelling); etc.

Additionally or alternatively, the Current Runtime can be estimated as afunction of, or adjusted in accordance with, one or more outdoorweather-related parameters (in addition to temperature). In someembodiments, the information implicating the outdoor weather-relatedparameter(s) over the Time Interval is included with the outdoor weatherdata obtained from a weather online data service (e.g., website) asdescribed above. Additional outdoor weather-related parameters include,but are not limited to: precipitation, wind speed, wind direction, solarinsolation, humidity, etc.

A new (or current) Total Runtime Value is determined at 206. In basicterms, the determined Total Runtime Value can be the sum or some othercorrelation of the previous Total Runtime Value and a value implicatedby the obtained outdoor weather data, such as the optional CurrentRuntime. The Total Runtime Value can be expressed in various terms, suchas, but not limited to, minutes, hours, unitless HDD/CDD value, etc. Itwill be recalled that when the prediction operation is first initiated(at 200), the Total Runtime Value is set to 0. Thus, in some embodimentsafter the first instance of obtaining outdoor temperature informationand the first instance of estimating Current Runtime (or a first CurrentRuntime), the Total Runtime Value will be the first Current Runtime.Later, a second Current Runtime will be estimated for a subsequent TimeInterval, with the Total Runtime Value then being determined as the sumof the first and second Current Runtimes. This cumulative techniquecontinues over time, with each newly-determined Total Runtime Valuebeing acted upon as described below.

At 208, a replacement status of the filter 34 is determined or estimatedas a function of at least the Total Runtime Value. The determinationcan, in some embodiments, be based upon, optionally be based solelyupon, a comparison of the Total Runtime Value with a Baseline Value. TheBaseline Value is indicative of a useful life of the filter 34, andreflects or represents an estimate of the length of time the filter 34can be exposed to (or handle) forced airflow and continue to perform atan acceptable level (e.g., is unlikely to have become clogged or dirtiedto an unacceptable level that otherwise negatively affects performanceof the HVAC system 22). The Baseline Value is expressed in the sameunits as the Total Runtime Value (e.g., hours, minutes, unitless, etc.),and can be pre-determined, ascertained, or derived by the predictionmodule 130 in various ways as described below.

In some embodiments, the Baseline Value can be, or can be based upon, apre-determined number or value that is stored by the prediction module130. The pre-determined value can be based upon the conventional threemonth replacement interval recommended for most residential 1″ HVAC airfilters. It is recognized that HVAC systems will operate more or lessfrequently in different climates; however, an average yearly operationtime for residential demand HVAC systems in the United States can bedetermined. Assuming the three month recommended replacement interval(or four replacements per year) is applicable to the United Statesaverage HVAC operation time, the Baseline Value can be set at one-fourthof the determined yearly average. Other pre-determined Baseline Valuesare equally acceptable, and can be derived by other strategies.

In other embodiments, the Baseline Value can be determined by theprediction module 130 with reference to one or more algorithms thataccount for one or more other parameters associated with the particulardwelling 20, the HVAC system 22, user preferences and/or the outdoorenvironment 26. One or more of these other parameters may beincorporated as a variable in the Baseline Value algorithm, or may beused to adjust a pre-determined starting Baseline Value that isotherwise assigned as above (e.g., based on national average HVACusages). These other parameters may relate to a likelihood that theparticular filter 34 may be exposed or subjected to elevated pollutionlevels (and thus more quickly become overtly clogged or dirty), userpreferences that implicate a desired deviation from a starting BaselineValue otherwise premised on four filter replacements per year,conditions that may implicate the HVAC system operating significantlymore or less frequently than an average HVAC system, etc. Informationrelating to one or more of the other parameters discussed below can beobtained a single time and stored in memory for use with all subsequentfilter prediction operations; at the start of a filter predictionoperation each time a new air filter is installed; periodically orrepeatedly during a prediction operation for a particular air filter(with the Baseline Value being adjusted during the predictionoperation); etc.

The Baseline Value can be determined as a function of, or adjusted inaccordance with, one or more pollution-related parameters, examples ofwhich are provided below. In some embodiments, the prediction module 130is configured or programmed to obtain information from the user and/orother sources (such as electronic online data services (e.g., website)as described above) implicating the particular pollution-relatedparameter. The so-received information can be employed as a variable inone or more Baseline Value determination algorithms and/or can be usedto adjust a pre-determined, preliminary Baseline Value (e.g., thepre-determined preliminary Baseline Value can be predicated upon anassumed level or value of the particular pollution-related parameter;where the actual level or value of the pollution-related parametervaries from the assumed level or value, the pre-determined preliminaryBaseline Value can be adjusted accordingly to arrive at the BaselineValue used for comparison with the Total Runtime Value). Exemplarypollution-related parameters include, but are not limited to: dustlevels in the outdoor environment 26 of the dwelling 20 (e.g., thedwelling 20 is near a dirt road); ground ozone levels at the outdoorenvironment 26 of the dwelling 20 (particularly relevant where the airfilter 34 is configured to capture ozone); fine particle levels(PM_(2.5)) in the outdoor environment 26 of the dwelling (particularlyrelevant where the air filter 34 is configured to capture fineparticles); pollen count levels in the outdoor environment 26 of thedwelling 20; presence and number of pets in the indoor environment 24 ofthe dwelling 20 (e.g., the air filter 34 may more quickly become dirtiedwith hair, dander, or other particles typically associate with pets andthus has a reduced useful life); number of people normally within theindoor environment 24 of the dwelling 20; window opening habits orpreferences of the user (e.g., outdoor air, often laden with airbornecontaminants, enters the indoor environment 24 via an open window andthe contaminants are ultimately captured at the air filter 34; thus,where a user prefers to keep windows open for extended periods of time,the air filter 34 may become saturated more quickly); an age of thedwelling 20 (e.g., older homes are more susceptible to mold or othercontaminants that may become airborne and are then captured by the airfilter 34); regular burning of candles within the indoor environment 24of the dwelling 20; burning of tobacco products within the indoorenvironment 24 of the dwelling 20; burning of incense within the indoorenvironment 24 of the dwelling 20; etc.

Alternatively or in addition, the Baseline Value can be determined as afunction of, or adjusted in accordance with, one or more HVAC-relatedparameters, examples of which are provided below. In some embodiments,the prediction module 130 is configured or programmed to obtaininformation from the user and/or other sources (such as electroniconline data services (e.g., website) as described above) implicating theparticular HVAC-related parameter. The so-received information can beemployed as a variable in one or more Baseline Value determinationalgorithms and/or can be used to adjust a pre-determined preliminaryBaseline Value (e.g., the pre-determined preliminary Baseline Value canbe predicated upon an assumed level or value of the particularHVAC-related parameter; where the actual level or value of theHVAC-related parameter varies from the assumed level or value, thepre-determined preliminary Baseline Value can be adjusted accordingly toarrive at the Baseline Value used for comparison with the Total RuntimeValue). Exemplary HVAC-related parameters include, but are not limitedto: a model or type of the air filter 34 (e.g., the particular airfilter 34 may or may not be constructed to capture certain types ofcontaminants); a dust-holding capacity of the air filter 34; filterchange interval recommended by the manufacturer of the air filter 34(e.g., the manufacturer may recommend filter replacement at an intervaldiffering from the “standard” three-month interval); efficiency of theHVAC system 22 (e.g., cooling efficiency, heating efficiency, or both);capacity of the HVAC system 22 (e.g., cooling capacity, heatingcapacity, or both); frequency the HVAC system 22 is serviced; an initialpressure drop across the air filter 34; etc.

Alternatively or in addition, the Baseline Value can be determined as afunction of, or adjusted in accordance with, one or more userpreference-related parameters, examples of which are provided below. Insome embodiments, the prediction module 130 is configured or programmedto obtain information from the user and/or other sources (such aselectronic online data services (e.g., website) as described above)implicating the particular user preference-related parameter. Theso-received information can be employed as a variable in one or moreBaseline Value determination algorithms and/or can be used to adjust apre-determined preliminary Baseline Value (e.g., the pre-determinedpreliminary Baseline Value can be predicated upon an assumed level orvalue of the particular user preference-related parameter; where theactual level or value of the user preference-related parameter variesfrom the assumed level or value, the pre-determined preliminary BaselineValue can be adjusted accordingly to arrive at the Baseline Value usedfor comparison with the Total Runtime Value). Exemplary userpreference-related parameters include, but are not limited to: interestin air quality (e.g., a user expressing a low concern over air qualityimplicates an increase in the Baseline Value (or less filter changes peryear); conversely, a user expressing a heightened or extreme concernover air quality implicates a decrease in the Baseline Value (or morefilter changes per year)); user preferred or targeted number of filterchanges per year; user fan operation preferences (e.g., user may preferto run the fan 32 continuously or nearly continuously, regardless ofwhether the HVAC system 22 is operating to cool or heat air); etc.

The Baseline Value can be determined as a function of, or adjusted inaccordance with, one or more other parameters in addition to thosediscussed above. For example, any of the weather-related parametersmentioned above in the context of estimating Current Runtime may beuseful in the Baseline Value determination. Additionally, CurrentRuntime can be estimated as a function of, or adjusted in accordancewith, one or more of the parameters mentioned above in the context ofthe determining the Baseline Value.

Regardless of how the Baseline Value is determined, comparison of theTotal Runtime Value with the Baseline Value at 208 can serve as thebasis for characterizing a replacement status of the air filter 34. Forexample, where the Total Runtime Value is found to approximate, equal,or exceed the Baseline Value, the prediction module 130 can beconfigured or programmed to determine or designate that the air filter34 should be replaced. Optionally, the systems and methods of thepresent disclosure can derive other characterizations of the filterreplacement status from the comparison. For example, where the TotalRuntime Value is within a predetermined percentage of the Baseline Value(e.g., within 10%), the replacement status can be characterized as theair filter nearing the end of its useful life.

Under circumstances where the determined replacement status does notimplicate immediately replacing the air filter 34, or under othercircumstances, the methods of the present disclosure can return to 202at which outdoor temperature information (and optionally additionalweather data) is again obtained for a subsequent Time Interval (e.g.,daily). The above-described process is repeated, with a new CurrentRuntime being estimated and added to the Total Runtime Value. The new orupdated Total Runtime Value is again compared to the Baseline Value togenerate a new or updated replacement status.

In some embodiments, the filter prediction operation for a particularair filter is terminated or ends once the replacement status indicatesthat the air filter 34 should be replaced. A notification is optionallydelivered to the user as described below, and it is assumed the airfilter 34 is replaced. In some embodiments, the filter predictionoperation is then re-initiated (e.g., automatically or in response to auser prompt) for predicting replacement status of the newly-installedair filter. Alternatively, the prediction module 130 can be configuredor programmed to re-initiate the filter prediction operation (includingto re-setting the Total Runtime Value to 0) only in response to a promptor request from the user indicative of a new air filter having beeninstalled. In other words, unless prompted by the user, the predictionmodule 130 will continue to estimate Current Runtime (optional), TotalRuntime Value and replacement status for the not-yet-replaced air filter34, optionally providing the user with information indicative of theextent to which the air filter 34 is beyond its useful life.

In some embodiments, the systems and methods of the present disclosurefurther include, at 210, providing (or conveying to) a user withinformation implicated by the determined replacement status. Forexample, where the determined replacement status indicates that the airfilter 34 should be replaced, the prediction module 130 can operate toprompt sending or providing a corresponding warning or message to theuser. Alternatively or in addition, the user can be advised as to aremaining useful life of the air filter 34. The messages, warnings orother information can take various forms. For example, a message orwarning can be displayed or generated on the computing device 100.Alternatively, a message can be sent to another device as selected bythe user, such as in the form of an email, text or electronic message.Alternatively or additionally, some embodiments relate to a mobileapplication for, for example, a homeowner where they could receivefilter replacement notifications through the application. In someembodiments, the user/homeowner can also indicate to the system when afilter has been replaced (to reset the total runtime).

As mentioned above, systems and methods of the present disclosure canentail reviewing outdoor temperature and other information at regularintervals as part of the Total Runtime Value determination. Onenon-limiting example of a loop analysis technique is provided in FIG. 6.At 300, the filter prediction operation is initiated. The Total RuntimeValue is set to 0. The variable “Start Time” is set to the current dateand time (e.g., as entered by the user or as electronically ascertainedby the prediction module 130 or other programming associated with thecomputing device 100).

At 302, the variable End Time is set as the day/time of the sum ofCurrent Start Time and a Time Interval. The Time Interval represents thefrequency at which outdoor temperature information (and possibly otheroutdoor weather data) is retrieved or obtained, and can be expressed interms of weeks, days, hours, minutes, etc. The Time Interval can be apre-determined value programmed with the prediction module 130, can beselected by user, can change over time, etc. Also, “Current Time Period”is established as initiating at the Start Time and ending at an EndTime. By way of example, if the day/time of the Start Time (as set at300) is Mar. 1, 2014/5:00 PM and the Time Interval is 24 hours (ordaily), the End Time as established at 302 is Mar. 2, 2014/5:00 PM; theCurrent Time Period is thus Mar. 1, 2014/5:00 PM-Mar. 2, 2014/5:00 PM.In other embodiments where the Time Interval is 24 hours (or daily),only the calendar date need be accounted for (i.e., the time of day doesnot have to be recorded or tracked).

At 304, outdoor temperature information, and possibly other outdoorweather data, is obtained for the Current Time Period (e.g., followingexpiration of the End Time), commensurate with the above descriptions.At 306, Current Runtime is optionally estimated for the Current TimePeriod in accordance with previous descriptions (e.g., as a function ofat least the obtained outdoor temperature information). The TotalRuntime Value is then set as the sum of Total Runtime Value and CurrentRuntime at 308. The newly determined Total Runtime Value is used as thebasis for determining a replacement status of the filter at 310, such asby comparing the Total Runtime Value with the Baseline Value asdescribed above. Under circumstances where the prediction operation forthe air filter will continue (e.g., the estimated replacement statuscharacterized the air filter as having remaining useful life), the StartTime is re-set as the End Time at 312. Continuing the above example, at312 the Start Time is set to Mar. 2, 2014/5:00 PM. The operation thenreturns to 302 at which the End Time and the Current Time Period arere-established as described above (i.e., the End Time is determined asthe sum of the Start Time and the Time Interval). Continuing the aboveexample (in which the Time Interval is 24 hours), upon returning to 302,the End Time is established as Mar. 3, 2014/5:00 PM, and the CurrentTime Period is Mar. 2, 2014/5:00 PM-Mar. 3, 2014/5:00 PM.

The techniques embodied by FIG. 6 are but one acceptable approach forloop-type tracking of Total Runtime Value. The systems and methods ofthe present disclosure can be implemented in a wide variety of otherlogic scenarios.

In several of the preceding examples, systems and methods of the presentdisclosure are described as optionally estimating a Current Runtime fromthe obtained outdoor weather data. In other embodiments, the obtainedoutdoor weather data embodies an indicator of fan runtime and can bedirectly added to the Total Runtime Value without modification. In otherwords, the step of estimating a Current Runtime or otherwise activelydesignating a Current Runtime from the obtained outdoor weather data isnot required by the present disclosure. For example, where the obtainedoutdoor weather data is an HDD or CDD value (otherwise implicating anoutdoor temperature relevant to a geographic region or locale of thedwelling 20 (FIG. 1)), the HDD or CDD value is directly added to theTotal Runtime Value.

FIG. 7 is a flow diagram illustrating non-limiting methods of thepresent disclosure in which Current Runtime need not be estimated. At400, the filter prediction operation is initiated. The Total RuntimeValue is set to 0. At 402, outdoor weather data is obtained for thedesignated period of time (or Time Interval) commensurate with the abovedescriptions. With the embodiment of FIG. 7, the obtained outdoorweather data is a value and can be an HDD or CDD value, or can be anyother temperature-related value that directly correlates with fanruntime.

At 404, the Total Runtime Value is set as the sum of Total Runtime Valueand the obtained outdoor weather value. Thus, with the embodiments ofFIG. 7, the Total Runtime Value is not necessarily a length of time, butrather is a value (e.g., a unitless value) that directly correlates witha length of time the fan 32 (FIG. 1) has operated. The newly determinedTotal Runtime Value is used as the basis for estimating a replacementstatus of the air filter at 406, such as by comparing the Total RuntimeValue with a Baseline Value. The Baseline Value can be predetermined orestimated as described above, and is expressed in terms correspondingwith the Total Runtime (e.g., the Total Runtime Value and the BaselineValue can both be expressed as a unitless HDD/CDD number). The steps ofobtaining additional weather data and “updating” the Total Runtime Valuecan subsequently be performed as described above. For example, thetechniques of FIG. 7 can operate to obtain an HDD/CDD value each day.The HDD/CDD values are accumulated (as the Total Runtime Value). Anend-of-life estimated or predicted filter replacement status can beobtained after the accumulated HDD and/or CDD values reach a baseline.

The systems and methods of the present disclosure optionally calibrateor adjust one or more of the algorithms described above (e.g.,algorithms useful in estimating Current Runtime or in determining aBaseline Value) based on feedback information. One of the challengeswhen developing prediction algorithms is measuring the accuracy ofpredictions. To measure the accuracy, feedback can be beneficial togauge the difference between the predictions and the actual parameters.The feedback can be used to tune the algorithm(s) and improve overallaccuracy of the system, which, in effect, allows the algorithm(s) to“learn” from past performance. In a laboratory or small user groupsetting, obtaining accuracy metrics for the algorithm(s) can beaccomplished by measuring the state of the air filter at time zero andagain at end-of-life as predicted by the algorithm(s), or by measuringthe state of the air filter at other points before or after thepredicted end-of-life and adjusting the analysis accordingly. Thesemeasurements can then help to tune the algorithm(s) as the environmentor other dynamic parameters change and must be compensated or accountedfor in the algorithm(s). When operating a system deployed to a largenumber of users, where access to the air filters is not guaranteed, newmethods can be identified to gather the feedback information. One methodof obtaining this information is through a simple user feedback surveyfollowing a filter reminder notice from the filter predictionalgorithm(s). In one embodiment, a user will compare the visualappearance of their actual air filter against a known reference scale,the output of which would be a ranking (e.g., number from 1 to 10)associated with the reference most like the appearance of the actual airfilter. One example of a reference scale useful with some embodiments ofthe present disclosure is a Peli-Robson contrast chart. In addition oralternatively, a user could send a digital image of their actual airfilter to a party monitoring implementation of the predictionalgorithm(s), could ship the air filter to the party, etc.

The systems and methods of the present disclosure provide a markedimprovement over previous designs. A simple, low-cost or no-cost meansof more accurately predicting and communication the need for filterchange in demand HVAC applications (as well as other HVAC application)is provided. The systems and methods of the present disclosure can beimplemented without requiring any alterations to an existing HVACsystem, such as installation of sensors, mechanical components, orelectrical components, and do not require a specialized HVAC systemcontroller. By predicting or estimating a replacement status of the HVACair filter based on outdoor temperature (and optionally otherparameters), users are provided with a credible basis for a filterreplacement notification.

FIGS. 8A and 8B illustrate how the systems and methods of the presentdisclosure provide improved filter change decisions in comparison to theconventional approach of changing the air filter every three months. Thegraphs of FIGS. 8A and 8B show predicted HVAC runtime based on publishedaverage climate date for the state of Iowa (from the National Oceanicand Atmospheric Administration) and using equations implicated by FIG.3; both graphs show identical runtime. The second data series in eachgraph shows the estimated pressure drop of the air filter based on anmodel correlating runtime, filter dust holding capacity, and filterpressure drop. The graphs of FIGS. 8A and 8B assume a clean filter isinstalled on January 1, and that four air filters are consumed duringthe year. The graph of FIG. 8A represents the conventional approach offilter replacement, and assumes that the filter change is even spaced atexactly 13 weeks (3 months). The graph of FIG. 8B reflects the resultingpressure drop when instead changing the air filter at the beginning ofweeks 9, 27 and 39 as would otherwise be implicated by the systems andmethods of the present disclosure in which air filter replacement isrecommended based upon estimated Total Runtime (that in turn isestimated as a function of outdoor temperature).

From an inspection of the two filter change approaches, several itemsstand out. First, simply changing the air filter ever three monthsresults in a wide range of final filter resistances, ranging from“barely used” to a 40% increase over the starting resistance. The secondair filter (of FIG. 8A) in particular shows a low change in pressuredrop because it is chiefly used during the transition out of heatingmode and into cooling mode when the average runtime is very low. Second,the balanced approach to runtime (of FIG. 8B) not only results in eachair filter being used an approximately identical amount, but it alsominimizes the maximum pressure drop that the HVAC system experiences, afactor that can be important in maintaining good airflow and equipmenthealth.

Although the present disclosure has been described with reference topreferred embodiments, workers skilled in the art will recognize thatchanges can be made in form and detail without departing from the spiritand scope of the present disclosure. This application is a continuationof application Ser. No. 15/532,186, now allowed, which was a nationalstage filing under 35 U.S.C. 371 of PCT/US2015/062591, which claimedpriority to U.S. Provisional Application 62/085,939, the disclosures ofall of which are incorporated by reference in their entirety herein.

What is claimed is:
 1. A computer-implemented method for estimating areplacement status of an air filter in a heating, ventilation, and airconditioning (HVAC) system, the method executed by one or moreprocessors of one or more computing devices, and comprising the stepsof: obtaining outdoor weather data for an outdoor environment related tothe HVAC system, wherein the outdoor weather data includes at leastoutdoor temperature information indicative of temperature at the outdoorenvironment of a dwelling that comprises the HVAC system; determining aTotal Runtime Value of a fan of the HVAC system based at least in partupon the obtained outdoor weather data; and estimating the replacementstatus of the air filter as a function of a comparison of the TotalRuntime Value with a Baseline Value and delivering a notification of thereplacement status of the air filter to a user.
 2. The method of claim1, wherein the step of obtaining outdoor weather data includeselectronically accessing an online data service.
 3. The method of claim2, wherein the step of obtaining outdoor weather data further includesretrieving the outdoor weather data from the online data service.
 4. Themethod of claim 2, wherein the online data service provides the outdoortemperature information for a plurality of locales, and wherein the stepof obtaining outdoor weather data further includes: identifying a one ofthe plurality of locales as corresponding with a locale of the HVACsystem.
 5. The method of claim 4, wherein the step of identifyingincludes: comparing a zip code of the HVAC system with a zip codeassociated with each of the plurality of locales.
 6. The method of claim1, wherein the outdoor temperature information includes an actualoutdoor temperature.
 7. The method of claim 1, wherein the outdoortemperature information includes a historical outdoor temperature. 8.The method of claim 1, wherein the outdoor weather data further includesone of a heating degree day value and a cooling degree day value.
 9. Themethod of claim 1, wherein the dwelling that comprises the HVAC systemis a single-family house.
 10. The method of claim 1, wherein the outdoorweather data is an average temperature over a first period of time. 11.The method of claim 1, wherein the step of obtaining outdoor weatherdata further includes obtaining weather information indicative of atleast a second weather parameter at the outdoor environment related tothe HVAC system apart from the outdoor temperature.
 12. The method ofclaim 11, wherein the second parameter is selected from the groupconsisting of humidity, wind speed, wind direction, solar insolation,precipitation, pollen count, ground ozone level, and airborneparticulate level.
 13. The method of claim 1, wherein the obtainedoutdoor weather data relates to a first period of time, the methodfurther comprising: estimating a Current Runtime of the fan over thefirst period of time based upon the obtained outdoor weather data;wherein the step of determining the Total Runtime Value includesdetermining the Total Runtime Value as a function of the CurrentRuntime.
 14. The method of claim 13, wherein the step of estimating theCurrent Runtime includes: comparing the outdoor temperature informationwith indoor temperature information otherwise indicative of an indoortemperature inside the dwelling that comprises the HVAC system; andestimating the Current Runtime as a function of the comparison.
 15. Themethod of claim 14, further comprising: receiving the indoor temperatureinformation from a user, wherein the indoor temperature information isselected from the group consisting of a heating mode set point and acooling mode set point.
 16. The method of claim 13, wherein the methodcomprises obtaining outdoor temperature information over time periods,and wherein a Current Runtime is estimated for each time period based onthe obtained outdoor temperature information.
 17. The method of claim13, wherein the Current Runtime is further estimated as a function of atleast a second weather parameter apart from the outdoor temperatureinformation, the second weather parameter selected from the groupconsisting of humidity, wind speed, wind direction, solar insolation,and precipitation.
 18. The method of claim 1, further comprising:determining the Baseline Value as a function of a userpreference-related parameter selected from the group consisting of airquality interest level, target number of annual filter changes, fanoperation, and window usage.
 19. The method of claim 1, wherein the HVACsystem is a residential, on-demand HVAC system.
 20. Acomputer-implemented system for estimating a replacement status of anair filter in a heating, ventilation, and air conditioning (HVAC)system, comprising: one or more processors of one or more computingdevices configured to: obtain outdoor weather data for an outdoorenvironment related to the HVAC system, wherein the outdoor weather dataincludes at least outdoor temperature information indicative oftemperature at the outdoor environment of a dwelling that comprises theHVAC system; determine a Total Runtime Value of a fan of the HVAC systembased at least in part upon the obtained outdoor weather data; andestimate the replacement status of the air filter as a function of acomparison of the Total Runtime Value with a Baseline Value and delivera notification of the replacement status of the air filter to a user.21. The computer-implemented system of claim 20, wherein the one or moreprocessors are further configured to electronically access an onlinedata service to obtain the outdoor weather data.
 22. Thecomputer-implemented system of claim 20, wherein the obtained outdoorweather data relates to a first period of time, and further wherein theone or more processors are further configured to: estimate a CurrentRuntime of the fan over the first period of time based upon the obtainedoutdoor weather data; determine the Total Runtime Value as a function ofthe Current Runtime.
 23. The computer-implemented system of claim 22,wherein the one or more processors are configured to estimate theCurrent Runtime by: comparing the outdoor temperature information withindoor temperature information otherwise indicative of an indoortemperature inside the dwelling that comprises the HVAC system; andestimating the Current Runtime as a function of the comparison.
 24. Thecomputer-implemented system of claim 22, wherein the one or moreprocessors are further configured to estimate the Current Runtime as afunction of at least a second weather parameter apart from the outdoortemperature information, the second weather parameter selected from thegroup consisting of humidity, wind speed, wind direction, solarinsolation, and precipitation.
 25. The computer-implemented system ofclaim 22, wherein the one or more processors are further configured toestimate the Current Runtime as a function of a dwelling parameterselected from the group consisting of square footage of the dwellingthat comprises the HVAC system, capacity of the HVAC system, age of thedwelling that comprises the HVAC system, shading of the dwelling thatcomprises the HVAC system, and energy efficiency of the dwelling thatcomprises the HVAC system.